Zobrazeno 1 - 10
of 321
pro vyhledávání: '"THOMAS, Hugues"'
How can a robot safely navigate around people exhibiting complex motion patterns? Reinforcement Learning (RL) or Deep RL (DRL) in simulation holds some promise, although much prior work relies on simulators that fail to precisely capture the nuances
Externí odkaz:
http://arxiv.org/abs/2410.10646
In this paper, we introduce a LiDAR-based robot navigation system, based on novel object-aware affordance-based costmaps. Utilizing a 3D object detection network, our system identifies objects of interest in LiDAR keyframes, refines their 3D poses wi
Externí odkaz:
http://arxiv.org/abs/2408.17034
In the field of deep point cloud understanding, KPConv is a unique architecture that uses kernel points to locate convolutional weights in space, instead of relying on Multi-Layer Perceptron (MLP) encodings. While it initially achieved success, it ha
Externí odkaz:
http://arxiv.org/abs/2405.13194
This paper presents the Embedding Pose Graph (EPG), an innovative method that combines the strengths of foundation models with a simple 3D representation suitable for robotics applications. Addressing the need for efficient spatial understanding in r
Externí odkaz:
http://arxiv.org/abs/2403.13777
Human following is a crucial feature of human-robot interaction, yet it poses numerous challenges to mobile agents in real-world scenarios. Some major hurdles are that the target person may be in a crowd, obstructed by others, or facing away from the
Externí odkaz:
http://arxiv.org/abs/2309.12479
Collision avoidance is key for mobile robots and agents to operate safely in the real world. In this work we present SAFER, an efficient and effective collision avoidance system that is able to improve safety by correcting the control commands sent b
Externí odkaz:
http://arxiv.org/abs/2209.11789
We present a method for generating, predicting, and using Spatiotemporal Occupancy Grid Maps (SOGM), which embed future semantic information of real dynamic scenes. We present an auto-labeling process that creates SOGMs from noisy real navigation dat
Externí odkaz:
http://arxiv.org/abs/2208.12602
Autor:
Chen, Meida, Hu, Qingyong, Yu, Zifan, Thomas, Hugues, Feng, Andrew, Hou, Yu, McCullough, Kyle, Ren, Fengbo, Soibelman, Lucio
Although various 3D datasets with different functions and scales have been proposed recently, it remains challenging for individuals to complete the whole pipeline of large-scale data collection, sanitization, and annotation. Moreover, the created da
Externí odkaz:
http://arxiv.org/abs/2203.09065
We present a novel method for generating, predicting, and using Spatiotemporal Occupancy Grid Maps (SOGM), which embed future information of dynamic scenes. Our automated generation process creates groundtruth SOGMs from previous navigation data. We
Externí odkaz:
http://arxiv.org/abs/2108.10585
We present unsupervised parameter learning in a Gaussian variational inference setting that combines classic trajectory estimation for mobile robots with deep learning for rich sensor data, all under a single learning objective. The framework is an e
Externí odkaz:
http://arxiv.org/abs/2102.11261